Abstract LB149: Luna Pathology: An integrated open source platform for computational pathology research
Authors: Doori Rose, Andrew Aukerman, Druv Patel, Arfath Pasha, Armaan Kohli, Ignacio Vázquez-García, Kevin Boehm, Rami Vanguri, Matthew Shabet, Vassiliki Mancoridis, Elizabeth Zakszewski, Benjamin Gross, Christopher Fong, Pegah Khosravi, Sohrab Shah, JianJiong Gao
Journal: Cancer Research (2022)

Abstract Pathologic review of tissue samples is a crucial step in cancer diagnosis and treatment planning. In recent years, quantitative analysis, including artificial intelligence (AI) techniques, have been applied to facilitate the evaluation of histopathologic images. Research in computational pathology comes with numerous engineering challenges: from management of whole slide images (WSIs) and their metadata, collection of expert annotations, data manipulation and feature extraction, to AI model training and evaluation of the results. These challenges span multiple departments and sets of expertise; there is a lack of an efficient system that helps manage and process histologic images and supports AI analysis. In particular, computational oncology requires significant compute resources. The need for an integrated platform with systematic processes to manage the job parameters and complex workflows that interface with multidisciplinary teams is critical for successful experiments and reproducible research. To address these challenges, we developed Luna Pathology, an open-source library and platform for computational pathology research. With flexibility, scalability, and FAIR data principles in mind, Luna Pathology was designed to provide 1) reusable workflows and modular tools for end-to-end pathology analysis 2) FAIR datasets for reproducible research and 3) a collaborative open-source research platform. Namely, Luna Pathology features support data management, annotation support, image processing, image and annotation visualization, feature extraction, and human-in-the-loop machine learning. A description of the Luna Pathology platform, its supporting computational infrastructure, and its integration within the standard computational pathology will be presented. Luna Pathology meets data processing and management needs, as well data analysis experiments and reproducible AI research on a flexible data platform. Its workflows have been deployed in colorectal, neuroblastoma and ovarian cancer projects at Memorial Sloan Kettering Cancer Center (MSK) to derive new insights from ~10,000 WSIs. We additionally present a view of the Luna Pathology platform as part of the institutional effort to bridge computational research and clinical care at MSK. The integrated open platform brings together a multidisciplinary team of pathologists, computational biologists, and bioinformatics engineers to more effectively collaborate on iterative large-scale computational pathology research. Citation Format: Doori Rose, Andrew Aukerman, Druv Patel, Arfath Pasha, Armaan Kohli, Ignacio Vázquez-García, Kevin Boehm, Rami Vanguri, Matthew Shabet, Vassiliki Mancoridis, Elizabeth Zakszewski, Benjamin Gross, Christopher Fong, Pegah Khosravi, Sohrab Shah, JianJiong Gao. Luna Pathology: An integrated open source platform for computational pathology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB149.